Exploiting universal nonlocal dispersion in optically active materials for spectro-polarimetric computational imaging
Abstract Recent years have seen significant advancements in exploring novel light-matter interactions such as hyperbolic dispersion within natural crystals. However, current studies have predominantly concentrated on local optical response of materials characterized by a dielectric tensor without sp...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-12-01
|
| Series: | eLight |
| Online Access: | https://doi.org/10.1186/s43593-024-00078-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Recent years have seen significant advancements in exploring novel light-matter interactions such as hyperbolic dispersion within natural crystals. However, current studies have predominantly concentrated on local optical response of materials characterized by a dielectric tensor without spatial dispersion. Here, we investigate the nonlocal response in optically-active crystals with screw symmetries, revealing their lossless, super-dispersive properties compared to traditional optical response functions. We leverage this universal nonlocal dispersion, i.e. the dispersion of optical rotatory power, to explore a novel spectral de-multiplexing scheme compared to conventional gratings, prisms and metasurfaces. We design and demonstrate an ‘Nonlocal-Cam’ - a camera that exploits nonlocal dispersion through sampling of polarized spectral states and the application of computational spectral reconstruction algorithms. The Nonlocal-Cam captures information in both laboratory and outdoor field experiments which is unavailable to traditional intensity cameras - the spectral texture of polarization. Merging the fields of nonlocal electrodynamics and computational imaging, our work paves the way for exploiting nonlocal optics of optically active materials in a variety of applications, from biological microscopy to physics-driven machine vision and remote sensing. |
|---|---|
| ISSN: | 2097-1710 2662-8643 |